Escalator passenger detection algorithm based on fast Adaboost training algorithm

Abstract

The invention discloses an escalator passenger detection algorithm based on a fast Adaboost training algorithm, which comprises steps: 1) a video image is acquired; 2) positive samples and negative samples are generated; 3) HOG features in the positive samples and the negative samples are extracted; 4) a fast Adaboost algorithm is used to train a classifier; 5) the obtained classifier is used forpassenger detection; and 6) a camshaft algorithm is used to track a human body. Through the algorithm of the invention, the training speed of an Adaboost classifier can be effectively improved, the time cost of the algorithm in situations in which multiple classifiers are used and multiple training classifiers are needed is greatly reduced.

Description

technical field [0001] The invention relates to the technical fields of image pattern recognition, machine learning and elevator production, in particular to an escalator passenger detection algorithm based on a fast Adaboost training algorithm. Background technique [0002] With the growth of the economy, the use of escalators has become more and more frequent, and there are more and more safety accidents on the escalators. Most of the safety accidents on the escalator are because when the state or behavior of the passengers on the escalator is abnormal, the escalator cannot respond and respond in time, but mechanically maintains the same working mode, resulting in the amplification of the abnormal situation and deterioration. It is impossible to arrange emergency workers to monitor the status of escalators and passengers beside each escalator. Therefore, it is necessary to utilize a video surveillance system to detect and track passengers on the escalator. The existing ...

AI Technical Summary

Problems solved by technology

It is almost impossible to train classifiers for different usage scenarios

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